{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NSS Tutorial\n",
    "\n",
    "Field\t| Type\n",
    "--------|-------\n",
    "ukprn\t| varchar(8)\n",
    "institution\t| varchar(100)\n",
    "subject\t| varchar(60)\n",
    "level\t| varchar(50)\n",
    "question\t| varchar(10)\n",
    "A_STRONGLY_DISAGREE\t| int(11)\n",
    "A_DISAGREE\t| int(11)\n",
    "A_NEUTRAL\t| int(11)\n",
    "A_AGREE\t| int(11)\n",
    "A_STRONGLY_AGREE\t| int(11)\n",
    "A_NA\t| int(11)\n",
    "CI_MIN\t| int(11)\n",
    "score\t| int(11)\n",
    "CI_MAX\t| int(11)\n",
    "response\t| int(11)\n",
    "sample\t| int(11)\n",
    "aggregate\t| char(1)\n",
    "\n",
    "National Student Survey 2012\n",
    "\n",
    "The National Student Survey <http://www.thestudentsurvey.com/> is presented to thousands of graduating students in UK Higher Education. The survey asks 22 questions, students can respond with STRONGLY DISAGREE, DISAGREE, NEUTRAL, AGREE or STRONGLY AGREE. The values in these columns represent PERCENTAGES of the total students who responded with that answer.\n",
    "\n",
    "The table `nss` has one row per institution, subject and question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " ····\n"
     ]
    }
   ],
   "source": [
    "import getpass\n",
    "import psycopg2\n",
    "from sqlalchemy import create_engine\n",
    "import pandas as pd\n",
    "pwd = getpass.getpass()\n",
    "engine = create_engine(\n",
    "    'postgresql+psycopg2://postgres:%s@localhost/sqlzoo' % (pwd))\n",
    "pd.set_option('display.max_rows', 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Check out one row\n",
    "\n",
    "The example shows the number who responded for:\n",
    "\n",
    "- question 1\n",
    "- at 'Edinburgh Napier University'\n",
    "- studying '(8) Computer Science'\n",
    "\n",
    "**Show the the percentage who STRONGLY AGREE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "nss = pd.read_sql_table('nss', engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A_STRONGLY_AGREE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33401</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       A_STRONGLY_AGREE\n",
       "33401                23"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nss.loc[(nss['question']=='Q01') & \n",
    "        (nss['institution']=='Edinburgh Napier University') & \n",
    "        (nss['subject']=='(8) Computer Science'),\n",
    "       ['A_STRONGLY_AGREE']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Calculate how many agree or strongly agree\n",
    "\n",
    "**Show the institution and subject where the score is at least 100 for question 15.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>institution</th>\n",
       "      <th>subject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8783</th>\n",
       "      <td>Kingston College</td>\n",
       "      <td>(I) Education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16659</th>\n",
       "      <td>Royal Holloway, University of London</td>\n",
       "      <td>(L) Geographical Studies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17359</th>\n",
       "      <td>Solihull College</td>\n",
       "      <td>(I) Education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18377</th>\n",
       "      <td>Stafford College</td>\n",
       "      <td>(D) Business and Administrative studies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27965</th>\n",
       "      <td>University of Southampton</td>\n",
       "      <td>(E) Mass Communications and Documentation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30395</th>\n",
       "      <td>University of Wolverhampton</td>\n",
       "      <td>(7) Mathematical Sciences</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38988</th>\n",
       "      <td>University of Leicester</td>\n",
       "      <td>(2) Subjects allied to Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40002</th>\n",
       "      <td>University of Newcastle upon Tyne</td>\n",
       "      <td>(E) Mass Communications and Documentation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42760</th>\n",
       "      <td>Bishop Grosseteste University College, Lincoln</td>\n",
       "      <td>(F) Languages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49116</th>\n",
       "      <td>Universities of East Anglia and Essex; Joint P...</td>\n",
       "      <td>(G) Historical and Philosophical studies</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             institution  \\\n",
       "8783                                    Kingston College   \n",
       "16659               Royal Holloway, University of London   \n",
       "17359                                   Solihull College   \n",
       "18377                                   Stafford College   \n",
       "27965                          University of Southampton   \n",
       "30395                        University of Wolverhampton   \n",
       "38988                            University of Leicester   \n",
       "40002                  University of Newcastle upon Tyne   \n",
       "42760     Bishop Grosseteste University College, Lincoln   \n",
       "49116  Universities of East Anglia and Essex; Joint P...   \n",
       "\n",
       "                                         subject  \n",
       "8783                               (I) Education  \n",
       "16659                   (L) Geographical Studies  \n",
       "17359                              (I) Education  \n",
       "18377    (D) Business and Administrative studies  \n",
       "27965  (E) Mass Communications and Documentation  \n",
       "30395                  (7) Mathematical Sciences  \n",
       "38988            (2) Subjects allied to Medicine  \n",
       "40002  (E) Mass Communications and Documentation  \n",
       "42760                              (F) Languages  \n",
       "49116   (G) Historical and Philosophical studies  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nss.loc[(nss['question']=='Q15') & (nss['score']>=100),\n",
    "        ['institution', 'subject']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Unhappy Computer Students\n",
    "\n",
    "**Show the institution and score where the score for '(8) Computer Science' is less than 50 for question 'Q15'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>institution</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1376</th>\n",
       "      <td>Blackburn College</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13881</th>\n",
       "      <td>North Lindsey College</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15323</th>\n",
       "      <td>Plymouth College of Art</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17382</th>\n",
       "      <td>Somerset College of Arts and Technology</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46588</th>\n",
       "      <td>University of Wales, Newport</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49006</th>\n",
       "      <td>Universities of East Anglia and Essex; Joint P...</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             institution  score\n",
       "1376                                   Blackburn College     45\n",
       "13881                              North Lindsey College     30\n",
       "15323                            Plymouth College of Art     47\n",
       "17382            Somerset College of Arts and Technology     48\n",
       "46588                       University of Wales, Newport     30\n",
       "49006  Universities of East Anglia and Essex; Joint P...     45"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nss.loc[(nss['question']=='Q15') & \n",
    "        (nss['subject']=='(8) Computer Science') & \n",
    "        (nss['score']<50), \n",
    "       ['institution', 'score']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. More Computing or Creative Students?\n",
    "\n",
    "**Show the subject and total number of students who responded to question 22 for each of the subjects '(8) Computer Science' and '(H) Creative Arts and Design'.**\n",
    "\n",
    "> _HINT_    \n",
    "> You will need to use SUM over the response column and GROUP BY subject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(8) Computer Science</td>\n",
       "      <td>10252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(H) Creative Arts and Design</td>\n",
       "      <td>33336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        subject  response\n",
       "0          (8) Computer Science     10252\n",
       "1  (H) Creative Arts and Design     33336"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(nss.loc[(nss['question']=='Q22') & \n",
    "         (nss['subject'].isin(\n",
    "             ['(8) Computer Science', '(H) Creative Arts and Design'])),\n",
    "        ['subject', 'response']]\n",
    "    .groupby('subject')\n",
    "    .sum()\n",
    "    .reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Strongly Agree Numbers\n",
    "\n",
    "**Show the subject and total number of students who A_STRONGLY_AGREE to question 22 for each of the subjects '(8) Computer Science' and '(H) Creative Arts and Design'.**\n",
    "\n",
    "> _HINT_    \n",
    "> The A_STRONGLY_AGREE column is a percentage. To work out the total number of students who strongly agree you must multiply this percentage by the number who responded (response) and divide by 100 - take the SUM of that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>n_strongly_agree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(8) Computer Science</td>\n",
       "      <td>3355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(H) Creative Arts and Design</td>\n",
       "      <td>11992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        subject  n_strongly_agree\n",
       "0          (8) Computer Science              3355\n",
       "1  (H) Creative Arts and Design             11992"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(nss.assign(n_strongly_agree=nss['response']*nss['A_STRONGLY_AGREE']/100)\n",
    "     .astype({'n_strongly_agree': 'int'})\n",
    "     .loc[(nss['question']=='Q22') &\n",
    "          (nss['subject'].isin(\n",
    "           ['(8) Computer Science', '(H) Creative Arts and Design'])),\n",
    "       ['subject', 'n_strongly_agree']]\n",
    "    .groupby('subject')\n",
    "    .sum()\n",
    "    .reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Strongly Agree, Percentage\n",
    "\n",
    "**Show the percentage of students who A_STRONGLY_AGREE to question 22 for the subject '(8) Computer Science' show the same figure for the subject '(H) Creative Arts and Design'.**\n",
    "\n",
    "Use the **ROUND** function to show the percentage without decimal places."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(8) Computer Science</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(H) Creative Arts and Design</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        subject   pct\n",
       "0          (8) Computer Science  33.0\n",
       "1  (H) Creative Arts and Design  36.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(nss.assign(n_sa=nss['A_STRONGLY_AGREE']*nss['response'])\n",
    "    .loc[(nss['question']=='Q22') &\n",
    "         (nss['subject'].isin(\n",
    "             ('(8) Computer Science', '(H) Creative Arts and Design'))),\n",
    "        ['subject', 'n_sa', 'response']]\n",
    "    .groupby('subject')\n",
    "    .sum()\n",
    "    .assign(pct=lambda x: round(x['n_sa']/x['response'], 0))\n",
    "    .reset_index()\n",
    "    .loc[:, ['subject', 'pct']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Scores for Institutions in Manchester\n",
    "\n",
    "**Show the average scores for question 'Q22' for each institution that include 'Manchester' in the name.**\n",
    "\n",
    "The column **score** is a percentage - you must use the method outlined above to multiply the percentage by the **response** and divide by the total response. Give your answer rounded to the nearest whole number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>institution</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Manchester Metropolitan University</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Manchester College</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>University of Manchester</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          institution  score\n",
       "0  Manchester Metropolitan University   81.0\n",
       "1              The Manchester College   72.0\n",
       "2            University of Manchester   83.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(nss.assign(score=nss['response']*nss['score'])\n",
    "    .loc[(nss['question']=='Q22') & (nss['institution'].str.contains('Manchester')),\n",
    "        ['institution', 'score', 'response']]\n",
    "    .groupby('institution')\n",
    "    .sum()\n",
    "    .assign(score=lambda x: round(x['score']/x['response'], 0))\n",
    "    .reset_index()\n",
    "    .loc[:, ['institution', 'score']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.Number of Computing Students in Manchester\n",
    "\n",
    "**Show the institution, the total sample size and the number of computing students for institutions in Manchester for 'Q01'.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>institution</th>\n",
       "      <th>sample</th>\n",
       "      <th>comp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Manchester Metropolitan University</td>\n",
       "      <td>6994</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Manchester College</td>\n",
       "      <td>537</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>University of Manchester</td>\n",
       "      <td>8065</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          institution  sample  comp\n",
       "0  Manchester Metropolitan University    6994   310\n",
       "1              The Manchester College     537    46\n",
       "2            University of Manchester    8065   180"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = (nss.loc[(nss['question']=='Q01') & \n",
    "            (nss['institution'].str.contains('Manchester')),\n",
    "            ['institution', 'sample', 'subject']].copy())\n",
    "a['comp'] = a['sample']\n",
    "a.loc[a['subject']!='(8) Computer Science', 'comp'] = 0\n",
    "a.groupby('institution')[['sample', 'comp']].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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